Algorithm for automatic diagnostic or prognostic monitoring of sleep/wake stages from movement and physiological signals.
- Device captures and differentiates unbiased sleep or wake information from different physiological signals.
- 99.8% less data storage space needed over current technology.
- Capable of use in privacy of patients' home.
Sleep disorders including insomnia, sleep apnea, hypersomnia, circadian rhythm disorders, restless leg syndrome, and parasomnia can have significant impact on health and safety. Interest in tracking sleep habits has popularized the use of wearable and mobile devices, as well as apps dedicated to sleep tracking. Classically, activity is measured by actigraphy derived from accelerometer data. This technology typically overestimates low activity. Alternatively, heart rate monitoring combined with actigraphy has been studied but this method suffers from low sensitivity.
Emory inventors have developed a device to monitor and acquire data for determining the sleep status of a subject by incorporating diverse physiological signals. The model mimics neural activities responding to external stimulus by using known sleep/awake stage information. Utilization of sleep/wake change points increases software efficiency and affords significantly reduced amounts of storage space to be required on a device.
- Method was validated using a dataset from an Emory University study. subgroup of participants (n = 102, men, mean age = 68.56, SD = 1.93) from the Emory Twin Study Follow-up recruited from the Vietnam Era Twin Registry.
- Software is developed.
Publication: Cakmak, Ayse S., et al. (2020). Sleep, 43(8).